Papers with early sequence truncation
Multi-word Tokenization for Sequence Compression (2023.emnlp-industry)
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| Challenge: | Large Language Models have proven successful at modelling tasks, but they are expensive and slow to scale. |
| Approach: | They propose a Multi-Word Tokenizer that represents frequent multi-word expressions as single tokens. |
| Outcome: | The proposed tokenizer is more robust across shorter sequence lengths, allowing for major speedups via early sequence truncation. |